random-forest

Description: Random forest is an ensemble of [[Decision Trees]] that improves accuracy and controls overfitting by averaging multiple trees trained on different subsets of data.

Key Points:

• Reduces overfitting compared to individual decision trees.
• Handles large datasets with higher dimensionality.
• Requires more computational resources.

Applications: Financial forecasting, image classification, healthcare diagnostics.